Update researchsimulation/InteractiveInterviewChatbot.py

researchsimulation/InteractiveInterviewChatbot.py

@@ -307,7 +307,6 @@ def tailor_answer_to_profile(agent_name, generic_answer, agent_question, user_pr
         
         task_description = f"""
         You are {agent_name}. Rewrite the following answer to match your personal communication style and tone preferences — **but only if the question is subjective or personal**.
-
         ---
         ### Original Generic Answer:
         {generic_answer}
@@ -315,14 +314,12 @@ def tailor_answer_to_profile(agent_name, generic_answer, agent_question, user_pr
         ---
         ### Question:
         "{agent_question}"
-
         ---
         ### *Communication Profile Reference:*
         - **Style:** {style}
         - **Tone:** {tone}
         - **Length:** {length}
         - **Topics:** {topics}
-
         ---
         ### Important Note on Factual Questions:
         If the question is a **factual or demographic one** (e.g., about age, name, location, occupation, birthplace), do **not** rewrite or stylise the answer. Just return the generic answer exactly as it is.
@@ -334,9 +331,7 @@ def tailor_answer_to_profile(agent_name, generic_answer, agent_question, user_pr
         - Where do you live?
         - What do you do?
         - Where were you born?
-
         Only rewrite if the question invites a **personal perspective, opinion, habit, or belief**.
-
         ---
         ### Hard Rules – You Must Follow These Without Exception:
         - If the question is factual → **return the generic answer unchanged**.
@@ -349,14 +344,12 @@ def tailor_answer_to_profile(agent_name, generic_answer, agent_question, user_pr
         - Do **not** include emojis, hashtags, placeholders, or third-person descriptions.
         - Maintain **narrative consistency** across responses to reflect a coherent personality.
         - Tailor phrasing, sentence structure, and vocabulary to fit your **persona** and **communication traits**.
-
         ---
         ### Personality Trait Alignment:
         Ensure your answer reflects these aspects of your personality profile:
         - Big Five Traits (e.g., Openness, Extraversion)
         - Values and Priorities (e.g., Risk Tolerance, Achievement Orientation)
         - Communication Preferences (e.g., Directness, Emotional Expressiveness)
-
         ---
         Final Output:
         - If the question is factual: return this answer exactly → **"{generic_answer}"**
@@ -399,7 +392,7 @@ def tailor_answer_to_profile(agent_name, generic_answer, agent_question, user_pr
 def validate_generic_answer(agent_name, agent_question, generic_answer, user_profile, processor_llm):
     logging.info("[validate_generic_answer] Entry")
     try:
-        is_valid = validate_response(
+        is_valid, feedback = validate_response(
             question=agent_question,
             answer=generic_answer,
             user_profile_str=str(user_profile),
@@ -407,13 +400,14 @@ def validate_generic_answer(agent_name, agent_question, generic_answer, user_pro
             interview_transcript_text="",  
             respondent_type=agent_name,
             ai_evaluator_agent=None,
-            processor_llm=processor_llm
+            processor_llm=processor_llm,
+            return_explanation=True
         )
-        logging.info(f"[validate_generic_answer] Result: {is_valid}")
-        return is_valid
+        logging.info(f"[validate_generic_answer] Result: {is_valid}, feedback: {feedback}")
+        return is_valid, feedback
     except Exception as e:
         logging.exception("[validate_generic_answer] Exception during validation")
-        return False
+        return False, "Exception during validation"
 
 def validate_styled_answer(agent_name, agent_question, styled_answer, user_profile, processor_llm):
     """
@@ -480,19 +474,30 @@ def ask_interview_question(respondent_agents_dict, last_active_agent, question,
 
             generic_answer = generate_generic_answer(agent_name, agent_question, respondent_agent)
 
-            if not validate_generic_answer(agent_name, agent_question, generic_answer, user_profile, processor_llm):
-                logging.warning(f"[validate_generic_answer]failed")
-                responses.append(f"**PreData Moderator**: Please ask {agent_name} again.")
+            is_valid, feedback = validate_generic_answer(agent_name, agent_question, generic_answer, user_profile, processor_llm)
+            if not is_valid:
+                logging.warning(f"[validate_generic_answer]failed: {feedback}")
+                responses.append(f"**PreData Moderator**: Please ask {agent_name} again. Reason: {feedback}")
                 continue
 
             tailored_attempts = 0
             max_tailored_attempts = 3
             tailored_answer = None
-            feedback = None
+            style_feedback = None
+            gen_feedback = feedback  # plausibility/relevance/accuracy feedback from generic validation
 
             while tailored_attempts < max_tailored_attempts:
+                # Combine feedback from generic and style validation for LLM
+                combined_feedback = None
+                if gen_feedback and style_feedback:
+                    combined_feedback = f"Generic feedback: {gen_feedback}. Style feedback: {style_feedback}"
+                elif gen_feedback:
+                    combined_feedback = gen_feedback
+                elif style_feedback:
+                    combined_feedback = style_feedback
+
                 styled = tailor_answer_to_profile(
-                    agent_name, generic_answer, agent_question, user_profile, respondent_agent, feedback=feedback
+                    agent_name, generic_answer, agent_question, user_profile, respondent_agent, feedback=combined_feedback
                 )
 
                 if len(styled) > 2000:
@@ -500,20 +505,19 @@ def ask_interview_question(respondent_agents_dict, last_active_agent, question,
                     tailored_attempts += 1
                     continue
 
-                is_valid, explanation = validate_styled_answer(
+                is_valid, style_feedback = validate_styled_answer(
                     agent_name, agent_question, styled, user_profile, processor_llm
                 )
                 if is_valid:
                     tailored_answer = styled
                     break
 
-                feedback = explanation  # Pass explanation as feedback for next attempt
                 tailored_attempts += 1
 
             if tailored_answer:
                 responses.append(f"**{agent_name}**: {tailored_answer}")
             else:
-                responses.append(f"**PreData Moderator**: Failed to stylise the response for {agent_name} after multiple attempts.")
+                responses.append(f"**PreData Moderator**: Failed to stylise the response for {agent_name} after multiple attempts. Last feedback: {style_feedback}")
 
         result = ["\n\n".join(responses)] if len(set(validated_questions.values())) == 1 else responses